Health & wellness — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 10 September 2026 and assigned it the closest of 31 fixed categories, at 100% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.
Growth
27 measurements spanning 35 days, net +850. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 27,273–28,451 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 27
Measured (UTC)
Subscribers
Change
11 Sept 2026, 09:38
28,285
+11
8 Sept 2026, 11:58
28,274
+10
5 Sept 2026, 07:16
28,264
-12
3 Sept 2026, 09:23
28,276
-8
2 Sept 2026, 00:57
28,284
-12
1 Sept 2026, 02:58
28,296
-7
31 Aug 2026, 00:04
28,303
+15
30 Aug 2026, 01:04
28,288
-14
29 Aug 2026, 00:32
28,302
-13
28 Aug 2026, 01:52
28,315
+35
26 Aug 2026, 22:57
28,280
+31
25 Aug 2026, 20:34
28,249
-2
24 Aug 2026, 19:54
28,251
+13
23 Aug 2026, 07:46
28,238
+410
21 Aug 2026, 17:46
27,828
+131
19 Aug 2026, 13:33
27,697
+8
18 Aug 2026, 14:27
27,689
+11
17 Aug 2026, 13:02
27,678
+99
16 Aug 2026, 02:06
27,579
+100
14 Aug 2026, 09:25
27,479
first reading
Engagement
60 posts held, back to 6 July 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 59 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
22.9%
avg views ÷ 28,285 subscribers
Avg views / post
6,470
33 posts measured
Reaction rate
1.01%
reactions ÷ views · ER floor
Posts in window
33
of 60 held
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views — note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.
What these figures were computed from
Window
Rolling 30 days · latest post in window 2 September 2026
Posts held
60 (6 July 2026 – 2 September 2026)
Views total
213,380
Reactions total
2,162
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
4 Sept 2026, 06:36 UTC
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
What this channel posts
Video runtime
2m 24s
Average length
2m 24s
Measured directly from 1 video with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
Reaction mix
4,935 reactions across 60 posts, in 23 distinct kinds. The most used accounts for 52.3% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
2,579
52.3%
👍
1,812
36.7%
👏
313
6.34%
👎
74
1.50%
🔥
48
0.973%
🙏
16
0.324%
🥰
15
0.304%
🤔
12
0.243%
💯
10
0.203%
👌
8
0.162%
😱
7
0.142%
❤🔥
6
0.122%
😁
6
0.122%
🤗
5
0.101%
🤣
5
0.101%
🤮
5
0.101%
🤬
4
0.081%
😨
3
0.061%
✍
2
0.041%
🍾
2
0.041%
3 further kinds
3
0.061%
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 60 of the 60 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 4,935 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 60 most recent posts we hold, published 6 July 2026 to 2 September 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
NON CONTA SOLO QUANTO PESO PERDI. CONTA ANCHE COME LO PERDI.
Per anni ci siamo sentiti ripetere che, per migliorare la salute metabolica, l’unica cosa davvero importante fosse dimagrire.
Ma un nuovo studio randomizzato pubblicato su Cell Metabolism racconta una storia più interessante.
I ricercatori hanno studiato 42 persone con:
👉 obesità metabolicamente non sana;
👉 prediabete;
👉 accumulo di grasso nel fegato.
…
STATE PERDENDO PESO CON SEMAGLUTIDE O TIRZEPATIDE? E LE OSSA, CHI LE STA GUARDANDO?
Di queste terapie parliamo continuamente: peso che scende, glicemia che migliora, metabolismo che cambia.
Ma c’è qualcosa che nelle foto del “prima e dopo” non vediamo: lo scheletro.
Un nuovo studio pubblicato sul Journal of Clinical Endocrinology & Metabolism ha valutato con DXA persone in trattamento con semaglutide o tirzepatide…
TOS E UMORE: COSA DICONO 67 STUDI RANDOMIZZATI?
Irritabilità, ansia, sonno disturbato, umore basso: durante la transizione menopausale il cervello può risentire profondamente delle variazioni ormonali.
Una nuova systematic review e meta-analisi di 67 RCT, per oltre 43.000 donne, ha valutato proprio l’effetto della terapia ormonale e dei fitoestrogeni sui sintomi psicologici della menopausa.
👉 Nel complesso, la ter…
GLP-1: PERDIAMO GRASSO. MA QUANTO TESSUTO MAGRO STIAMO SACRIFICANDO?
È una delle domande che considero più importanti quando parliamo di grandi dimagrimenti con i farmaci GLP-1.
Una nuova systematic review con network meta-analysis ha raccolto 43 studi randomizzati controllati, 3.379 partecipanti e 17 diversi trattamenti/dosaggi, analizzando direttamente la composizione corporea.
Il risultato è molto chiaro su un …
SEMAGLUTIDE, TIRZEPATIDE O CHIRURGIA: CHI OTTIENE I RISULTATI MIGLIORI?
Uno studio appena pubblicato su The Lancet Diabetes & Endocrinology ha analizzato 45.093 persone con obesità e diabete tipo 2 trattate con semaglutide, tirzepatide oppure sleeve gastrectomy.
L’obiettivo era molto ambizioso: raggiungere contemporaneamente, dopo un anno,
👉 almeno −20% del peso corporeo
👉 HbA1c <5,7%
📊 Quanti ci sono riusciti?
💉…
🔥 ESTROGENI VAGINALI DOPO UN TUMORE AL SENO: POSSIAMO USARLI?
È una delle domande più difficili in menopausa.
Una donna ha avuto un tumore al seno sensibile agli estrogeni (ER+), assume un inibitore dell’aromatasi e soffre di secchezza vaginale, bruciore o dolore durante i rapporti.
👉 Se i trattamenti non ormonali non bastano, possiamo utilizzare estrogeni vaginali?
Un nuovo studio randomizzato, VEMORA, ha confron…
💊 Farmaci GLP-1 e muscolo: il problema silenzioso delle proteine
Ne parliamo tanto per il peso, poco per il muscolo. Una revisione appena uscita su Advances in Therapy mette in fila i dati su cosa succede all'alimentazione durante le terapie con agonisti GLP-1 e GIP/GLP-1: questi farmaci riducono fortemente l'appetito, quindi le calorie totali — e insieme alle calorie può scendere anche la quantità assoluta di prote…
💪 Quale esercizio abbassa di più la glicata dopo i 60?
Se hai più di 60 anni e il diabete di tipo 2, "fai movimento" te lo dicono tutti. Ma quale movimento?
Una nuova revisione sistematica con meta-analisi a rete — solo studi randomizzati, su adulti anziani con diabete di tipo 2 — ha confrontato cinque modalità di allenamento: aerobico continuo, forza, combinato, esercizio mente-corpo (yoga e tai chi) e HIIT, gli i…
🔥 TIRZEPATIDE: DOPO AVER PERSO MOLTO PESO, COSA SUCCEDE SE RIDUCIAMO LA DOSE O SOSPENDIAMO?
Questa, secondo me, è una delle domande più importanti nell’era dei GLP-1.
Sappiamo ormai che farmaci come tirzepatide possono produrre perdite di peso impressionanti.
Ma il vero problema arriva dopo:
👉 dobbiamo continuare per sempre alla dose massima?
👉 possiamo ridurre la dose?
👉 e cosa succede se sospendiamo?
Lo studio …
Sovrappeso e poco muscolo: la bilancia non lo vede 🥗💪
Si può avere peso in eccesso e, nello stesso tempo, troppo poca massa muscolare. È la cosiddetta obesità sarcopenica, e il peso sulla bilancia non la rivela ed è quella di cui ho paura io se la gente che usa i farmaci agonisti del GLP-1 non ricevono le adeguate informazioni!
Uno studio appena pubblicato sul Journal of Diabetes and Metabolic Disorders ha analizza…
❤54👍27👏4
Showing the 12 most recent of 60 posts we hold for @drcristinatomasi. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Posts edited after publishing
@drcristinatomasi edited 1 post after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.
An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.
First edit seen
17 August 2026
Most recent edit
17 August 2026
Channels Telegram recommends alongside this one
Telegram’s own answer, not this register’s. When this register asks Telegram’s API what is similar to this channel, this is the list it returns, in the exact order Telegram returns it — never re-sorted by subscribers or by anything else this register measures. The relationship, and the order, are Telegram’s; we record them and date them, and make no claim of our own about which of these channels actually resemble this one.
Read from Telegram’s recommendation API, most recently 9 September 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.
Appears in Telegram’s recommendations for other channels
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
Dott. GABRIELE PRINZI - il sistema immunitario è argomento da complottisti😎 @dottgabrieleprinzi_official · 29,487 Telegram ranks this channel #27 of 72 here — alongside 71 others — read 8 September 2026
This channel appears in 1 seed channel's Telegram-generated recommendation list in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
Cite this entry
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 11 September 2026 — this
entry's latest reading, not the date you are reading this.
“Dr. Cristina Tomasi” (@drcristinatomasi), 28,285 subscribers as measured 11 September 2026. Telegram Register, tgregister.com/channel/drcristinatomasi.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.